Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, tempera...

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Veröffentlicht in:	Computer physics communications Jg. 295; S. 108969
Hauptverfasser:	Linghu, Jiale, Dong, Hao, Gao, Weifeng, Nie, Yufeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.02.2024
Schlagworte:	Artificial neural network Intelligent optimization algorithm Nonlinear thermal performances Random heterogeneous materials Wavelet transform Wavelet transform Artificial neural network Intelligent optimization algorithm Random heterogeneous materials Nonlinear thermal performances
ISSN:	0010-4655, 1879-2944
Online-Zugang:	Volltext
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